Search ga_s2am_ard_3 whole collection

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n to (exclusive):

Time when dataset was created (processed) From

Time when dataset was created (processed) to (exclusive)

The raw CRS string as it appears in metadata\n\n(e.g. ‘epsg:32654’)\n:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n from:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n to (exclusive):

File format (GeoTiff, NetCDF):

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres)\n to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

Latitude range from:

Latitude range to (exclusive):

Longitude range from:

Longitude range to (exclusive):

Full metadata document:

Metadata type name of dataset:

ID of a metadata type:

Spatial reference code from the provider.\nFor Sentinel it is MGRS code.\n:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n to (exclusive):

Unique identifier for a datastrip relative to a given Datatake.\n\n(e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’)\n:

ESA product URI, with the '.SAFE' ending removed.\n\n(e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845')\n:

Granule ID according to the ESA naming convention\n\n(e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’)\n:

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2022-09-11 12:04:24	ga_s2am_ard_3-2-1_49KHQ_2022-09-11_final.stac-item
2022-09-11 12:04:29	ga_s2am_ard_3-2-1_49KGQ_2022-09-11_final.stac-item
2022-09-11 12:04:42	ga_s2am_ard_3-2-1_49KGP_2022-09-11_final.stac-item
2022-09-11 12:04:57	ga_s2am_ard_3-2-1_49JGN_2022-09-11_final.stac-item
2022-09-11 12:05:11	ga_s2am_ard_3-2-1_49JGM_2022-09-11_final.stac-item
2022-09-11 12:05:25	ga_s2am_ard_3-2-1_49JGL_2022-09-11_final.stac-item
2022-09-11 12:05:40	ga_s2am_ard_3-2-1_49JGK_2022-09-11_final.stac-item
2022-09-11 12:05:54	ga_s2am_ard_3-2-1_49JGJ_2022-09-11_final.stac-item
2022-09-11 12:06:06	ga_s2am_ard_3-2-1_50JKN_2022-09-11_final.stac-item
2022-09-11 12:06:20	ga_s2am_ard_3-2-1_50JKM_2022-09-11_final.stac-item
2022-09-11 12:06:33	ga_s2am_ard_3-2-1_50JKL_2022-09-11_final
2022-09-14 12:14:04	ga_s2am_ard_3-2-1_49KHR_2022-09-14_final.stac-item
2022-09-14 12:14:08	ga_s2am_ard_3-2-1_49KGR_2022-09-14_final.stac-item
2022-09-14 12:14:18	ga_s2am_ard_3-2-1_49KHQ_2022-09-14_final.stac-item
2022-09-14 12:14:23	ga_s2am_ard_3-2-1_49KGQ_2022-09-14_final.stac-item
2022-09-14 12:14:37	ga_s2am_ard_3-2-1_49KGP_2022-09-14_final.stac-item
2022-09-14 12:14:51	ga_s2am_ard_3-2-1_49JGN_2022-09-14_final.stac-item
2022-09-21 12:04:21	ga_s2am_ard_3-2-1_49KHQ_2022-09-21_final.stac-item
2022-09-21 12:04:26	ga_s2am_ard_3-2-1_49KGQ_2022-09-21_final.stac-item
2022-09-21 12:04:36	ga_s2am_ard_3-2-1_49KGP_2022-09-21_final.stac-item
2022-09-21 12:04:42	ga_s2am_ard_3-2-1_49KGP_2022-09-21_final.stac-item
2022-09-21 12:04:53	ga_s2am_ard_3-2-1_49JGN_2022-09-21_final.stac-item
2022-09-21 12:05:08	ga_s2am_ard_3-2-1_49JGM_2022-09-21_final.stac-item
2022-09-21 12:05:22	ga_s2am_ard_3-2-1_49JGL_2022-09-21_final.stac-item
2022-09-21 12:05:37	ga_s2am_ard_3-2-1_49JGK_2022-09-21_final.stac-item
2022-09-21 12:05:51	ga_s2am_ard_3-2-1_49JGJ_2022-09-21_final.stac-item
2022-09-21 12:06:03	ga_s2am_ard_3-2-1_50JKN_2022-09-21_final.stac-item
2022-09-21 12:06:17	ga_s2am_ard_3-2-1_50JKM_2022-09-21_final.stac-item
2022-09-21 12:06:30	ga_s2am_ard_3-2-1_50JKL_2022-09-21_final
2022-09-24 12:14:02	ga_s2am_ard_3-2-1_49KHR_2022-09-24_final.stac-item
2022-09-24 12:14:05	ga_s2am_ard_3-2-1_49KGR_2022-09-24_final.stac-item
2022-09-24 12:14:16	ga_s2am_ard_3-2-1_49KHQ_2022-09-24_final.stac-item
2022-09-24 12:14:20	ga_s2am_ard_3-2-1_49KGQ_2022-09-24_final.stac-item
2022-09-24 12:14:34	ga_s2am_ard_3-2-1_49KGP_2022-09-24_final.stac-item
2022-09-24 12:14:48	ga_s2am_ard_3-2-1_49JGN_2022-09-24_final.stac-item
2022-10-01 12:04:42	ga_s2am_ard_3-2-1_49KGP_2022-10-01_final.stac-item
2022-10-01 12:04:53	ga_s2am_ard_3-2-1_49JGN_2022-10-01_final.stac-item
2022-10-01 12:05:08	ga_s2am_ard_3-2-1_49JGM_2022-10-01_final.stac-item
2022-10-01 12:05:22	ga_s2am_ard_3-2-1_49JGL_2022-10-01_final.stac-item
2022-10-01 12:05:36	ga_s2am_ard_3-2-1_49JGK_2022-10-01_final.stac-item
2022-10-01 12:05:51	ga_s2am_ard_3-2-1_49JGJ_2022-10-01_final.stac-item
2022-10-01 12:06:03	ga_s2am_ard_3-2-1_50JKN_2022-10-01_final.stac-item
2022-10-01 12:06:17	ga_s2am_ard_3-2-1_50JKM_2022-10-01_final.stac-item
2022-10-01 12:06:30	ga_s2am_ard_3-2-1_50JKL_2022-10-01_final
2022-10-04 12:14:02	ga_s2am_ard_3-2-1_49KHR_2022-10-04_final.stac-item
2022-10-04 12:14:05	ga_s2am_ard_3-2-1_49KGR_2022-10-04_final.stac-item
2022-10-04 12:14:16	ga_s2am_ard_3-2-1_49KHQ_2022-10-04_final.stac-item
2022-10-04 12:14:20	ga_s2am_ard_3-2-1_49KGQ_2022-10-04_final.stac-item
2022-10-04 12:14:34	ga_s2am_ard_3-2-1_49KGP_2022-10-04_final.stac-item
2022-10-04 12:14:48	ga_s2am_ard_3-2-1_49JGN_2022-10-04_final.stac-item
2022-11-20 12:04:19	ga_s2am_ard_3-2-1_49KGQ_2022-11-20_final.stac-item
2022-11-20 12:04:32	ga_s2am_ard_3-2-1_49KGP_2022-11-20_final.stac-item
2022-11-20 12:04:47	ga_s2am_ard_3-2-1_49JGN_2022-11-20_final.stac-item
2022-11-20 12:05:01	ga_s2am_ard_3-2-1_49JGM_2022-11-20_final.stac-item
2022-11-20 12:05:16	ga_s2am_ard_3-2-1_49JGL_2022-11-20_final.stac-item
2022-11-20 12:05:30	ga_s2am_ard_3-2-1_49JGK_2022-11-20_final.stac-item
2022-11-20 12:05:45	ga_s2am_ard_3-2-1_49JGJ_2022-11-20_final.stac-item
2022-11-20 12:05:56	ga_s2am_ard_3-2-1_50JKN_2022-11-20_final.stac-item
2022-11-20 12:06:11	ga_s2am_ard_3-2-1_50JKM_2022-11-20_final.stac-item
2022-11-20 12:06:23	ga_s2am_ard_3-2-1_50JKL_2022-11-20_final
2022-11-30 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2022-11-30_final.stac-item
2022-11-30 12:04:31	ga_s2am_ard_3-2-1_49KGP_2022-11-30_final.stac-item
2022-11-30 12:04:46	ga_s2am_ard_3-2-1_49JGN_2022-11-30_final.stac-item
2022-11-30 12:05:01	ga_s2am_ard_3-2-1_49JGM_2022-11-30_final.stac-item
2022-11-30 12:05:15	ga_s2am_ard_3-2-1_49JGL_2022-11-30_final.stac-item
2022-11-30 12:05:29	ga_s2am_ard_3-2-1_49JGK_2022-11-30_final.stac-item
2022-11-30 12:05:44	ga_s2am_ard_3-2-1_49JGJ_2022-11-30_final.stac-item
2022-11-30 12:05:55	ga_s2am_ard_3-2-1_50JKN_2022-11-30_final.stac-item
2022-11-30 12:06:10	ga_s2am_ard_3-2-1_50JKM_2022-11-30_final.stac-item
2022-11-30 12:06:22	ga_s2am_ard_3-2-1_50JKL_2022-11-30_final
2023-01-09 12:04:17	ga_s2am_ard_3-2-1_49KGQ_2023-01-09_final.stac-item
2023-01-09 12:04:30	ga_s2am_ard_3-2-1_49KGP_2023-01-09_final.stac-item
2023-01-09 12:04:44	ga_s2am_ard_3-2-1_49JGN_2023-01-09_final.stac-item
2023-01-09 12:04:59	ga_s2am_ard_3-2-1_49JGM_2023-01-09_final.stac-item
2023-02-25 11:56:41	ga_s2am_ard_3-2-1_50HKK_2023-02-25_nrt
2023-02-25 11:56:43	ga_s2am_ard_3-2-1_49HGE_2023-02-25_nrt
2023-02-25 11:56:55	ga_s2am_ard_3-2-1_50HKJ_2023-02-25_nrt
2023-02-25 11:56:57	ga_s2am_ard_3-2-1_49HGD_2023-02-25_nrt
2023-02-25 11:57:10	ga_s2am_ard_3-2-1_50HKH_2023-02-25_nrt
2023-02-25 11:57:12	ga_s2am_ard_3-2-1_49HGC_2023-02-25_nrt
2023-02-25 11:57:22	ga_s2am_ard_3-2-1_50HKG_2023-02-25_nrt
2023-02-25 11:57:23	ga_s2am_ard_3-2-1_49HGB_2023-02-25_nrt
2023-02-28 12:04:16	ga_s2am_ard_3-2-1_49KGQ_2023-02-28_final
2023-02-28 12:04:26	ga_s2am_ard_3-2-1_49KGP_2023-02-28_final
2023-02-28 12:04:32	ga_s2am_ard_3-2-1_49KGP_2023-02-28_final
2023-02-28 12:04:43	ga_s2am_ard_3-2-1_49JGN_2023-02-28_final
2023-02-28 12:04:58	ga_s2am_ard_3-2-1_49JGM_2023-02-28_final
2023-02-28 12:05:12	ga_s2am_ard_3-2-1_49JGL_2023-02-28_final
2023-02-28 12:05:27	ga_s2am_ard_3-2-1_49JGK_2023-02-28_final
2023-02-28 12:05:41	ga_s2am_ard_3-2-1_49JGJ_2023-02-28_final
2023-02-28 12:05:53	ga_s2am_ard_3-2-1_50JKN_2023-02-28_final
2023-02-28 12:06:07	ga_s2am_ard_3-2-1_50JKM_2023-02-28_final
2023-02-28 12:06:10	ga_s2am_ard_3-2-1_49JGG_2023-02-28_nrt
2023-02-28 12:06:20	ga_s2am_ard_3-2-1_50JKL_2023-02-28_final
2023-02-28 12:06:21	ga_s2am_ard_3-2-1_49JGF_2023-02-28_nrt
2023-03-13 12:13:51	ga_s2am_ard_3-2-1_49KHR_2023-03-13_final
2023-03-13 12:13:55	ga_s2am_ard_3-2-1_49KGR_2023-03-13_final
2023-03-13 12:14:05	ga_s2am_ard_3-2-1_49KHQ_2023-03-13_final
2023-03-13 12:14:09	ga_s2am_ard_3-2-1_49KGQ_2023-03-13_final
2023-03-13 12:14:23	ga_s2am_ard_3-2-1_49KGP_2023-03-13_final

Return to ga_s2am_ard_3 whole collection overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

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